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Scripts2026年4月1日·1 分钟阅读

Ragas — Evaluate RAG & LLM Applications

Name: Ragas — Evaluate RAG & LLM Applications
Author: TokRepo精选

Ragas evaluates LLM applications with objective metrics, test data generation, and data-driven insights. 13.2K+ GitHub stars. RAG evaluation, auto test generation. Apache 2.0.

TokRepo精选 · Community

快速使用

先拿来用，再决定要不要深挖

这里应该同时让用户和 Agent 知道第一步该复制什么、安装什么、落到哪里。

# Install
pip install ragas

# Quick start — generate a RAG evaluation project
ragas quickstart rag_eval -o ./my-project

# Or evaluate in Python
python -c "
from ragas.metrics import faithfulness, answer_relevancy
from ragas import evaluate
# Evaluate your RAG pipeline outputs
# results = evaluate(dataset, metrics=[faithfulness, answer_relevancy])
print('Ragas ready! Define your dataset and run evaluate().')
"

介绍

Ragas is a toolkit for supercharging LLM application evaluations with objective metrics, intelligent test data generation, and data-driven insights. With 13,200+ GitHub stars and Apache 2.0 license, Ragas provides LLM-based and traditional evaluation metrics for RAG pipelines (faithfulness, answer relevancy, context precision/recall), automatic test dataset generation from your documents, seamless integration with LangChain and observability tools, and feedback loops to leverage production data for improvement.

Best for: Teams building RAG pipelines who need systematic evaluation and quality measurement Works with: Claude Code, OpenAI Codex, Cursor, Gemini CLI, Windsurf Integrations: LangChain, LlamaIndex, Langfuse, Phoenix

Key Features

RAG metrics: Faithfulness, answer relevancy, context precision/recall
Test data generation: Auto-create evaluation datasets from documents
LLM + traditional metrics: Both AI-judged and deterministic scoring
Production feedback loops: Use real data to improve quality
LangChain integration: Evaluate chains and retrievers directly
Async scoring: Fast parallel evaluation with any LLM provider

FAQ

Q: What is Ragas? A: Ragas is an LLM evaluation toolkit with 13.2K+ stars. Objective metrics for RAG (faithfulness, relevancy), auto test generation, LangChain integration. Apache 2.0.

Q: How do I install Ragas? A: pip install ragas. Quick start with ragas quickstart rag_eval -o ./my-project.

🙏

来源与感谢

Created by Exploding Gradients. Apache 2.0. explodinggradients/ragas — 13,200+ GitHub stars

Ragas — Evaluate RAG & LLM Applications

先拿来用，再决定要不要深挖

Key Features

FAQ

来源与感谢

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